TensorFlow Lite runs machine learning inference by traversing a
computational graph where the nodes represent computations that are
part of the model and edges represent the dependence of values between
different computations [1]}. We inspect the TensorFlow
Lite source code to find that while running multi-threaded inference
through a single interpreter, it is only the individual computations
corresponding to the nodes of the computational graph that execute on
multiple threads, while the invocation of these nodes happens
sequentially on a single thread. Thus, with a single interpreter
not more than one node of the computational graph can run at a given time.
